In eukaryotes, crossovers together with sister chromatid cohesion maintain physical association between homologous chromosomes, ensuring accurate chromosome segregation during meiosis I and resulting in exchange of ge...In eukaryotes, crossovers together with sister chromatid cohesion maintain physical association between homologous chromosomes, ensuring accurate chromosome segregation during meiosis I and resulting in exchange of genetic information between homologues. The Arabidopsis PTD （Parting Dancers） gene affects the level of meiotic crossover formation, but its functional relationships with other core meiotic genes, such as AtSP011-1, AtRAD51, and AtMSH4, are unclear; whether PTD has other functions in meiosis is also unknown. To further analyze PTD function and to test for epistatic relationships, we compared the meiotic chromosome behaviors ofAtspoll-1 ptd and Atrad51 ptd double mutants with the relevant single mutants. The results suggest that PTD functions downstream of AtSP011-1 and AtRAD51 in the meiotic recombination pathway. Furthermore, we found that meiotic defects in rck pM and Atmsh4 ptd double mutants showed similar meiotic phenotypes to those of the relevant single mutants, providing genetic evidences for roles of PTD and RCK in the type I crossovers pathway. Moreover, we employed a pollen tetrad-based fluorescence method and found that the meiotic crossover frequencies in two genetic intervals were significantly reduced from 6.63% and 22.26% in wild-type to 1.14% and 6.36%, respectively, in the ptd^2 mutant. These results revealed new aspects of PTD function in meiotic crossover formation.展开更多
AIM:To evaluate the association between the geneticpolymorphisms and haplotypes of the ITGA1 gene and the risk of gastric cancer.METHODS:The study subjects were 477 age-and sex-matched case-control pairs.Genotyping wa...AIM:To evaluate the association between the geneticpolymorphisms and haplotypes of the ITGA1 gene and the risk of gastric cancer.METHODS:The study subjects were 477 age-and sex-matched case-control pairs.Genotyping was performed for 15 single nucleotide polymorphisms(SNPs)in ITGA1.The associations between gastric cancer and these SNPs and haplotypes were analyzed with multivariate conditional logistic regression models.Multiple testing corrections were carried out following methodology for controlling the false discovery rate.Gene-based association tests were performed using the versatile gene-based association study(VEGAS)method.RESULTS:In the codominant model,the ORs for SNPs rs2432143(1.517;95%CI:1.144-2.011)and rs2447867(1.258;95%CI:1.051-1.505)were statistically significant.In the dominant model,polymorphisms of rs1862610 and rs2447867 were found to be significant risk factors,with ORs of 1.337(95%CI:1.029-1.737)and 1.412(95%CI:1.061-1.881),respectively.In the recessive model,only the rs2432143 polymorphism was significant(OR=1.559,95%CI:1.150-2.114).The C-C type of ITGA1 haplotype block 2 was a significant protective factor against gastric cancer in the both codominant model(OR=0.602,95%CI:0.212-0.709,P=0.021)and the dominant model(OR=0.653,95%CI:0.483-0.884).The ITGA1 gene showed a significant gene-based association with gastric cancer in the VEGAS test.In the dominant model,the A-T type of ITGA1 haplotype block 2 was a significant risk factor(OR=1.341,95%CI:1.034-1.741).SNP rs2447867 might be related to the severity of gastric epithelial injury due to inflammation and,thus,to the risk of developing gastric cancer.CONCLUSION:ITGA1 gene SNPs rs1862610,rs2432143,and rs2447867 and the ITGA1 haplotype block that includes SNPs rs1862610 and rs2432143 were significantly associated with gastric cancer.展开更多
AIM: To evaluate the association between genetic polymorphisms of the gene encoding AMP-activated protein kinase (PRKAA1) and the risk of gastric cancer.
Epigenetic alterations are widespread in cancer and can complement genetic alterations to influence cancer progression and treatment outcome.To determine the potential contribution of DNA methylation alterations to tu...Epigenetic alterations are widespread in cancer and can complement genetic alterations to influence cancer progression and treatment outcome.To determine the potential contribution of DNA methylation alterations to tumor phenotype in non-small cell lung cancer(NSCLC)in both smoker and never-smoker patients,we performed genome-wide profiling of DNA methylation in 17 primary NSCLC tumors and 10 matched normal lung samples using the complementary assays,methylated DNA immunoprecipitation sequencing(MeDIP-seq)and methylation sensitive restric-tion enzyme sequencing(MRE-seq).We reported recurrent methylation changes in the promoters of several genes,many previously implicated in cancer,including FAM83A and SEPT9(hy-pomethylation),as well as PCDH7,NKX2-1,and SOX17(hypermethylation).Although many methylation changes between tumors and their paired normal samples were shared across patients,several were specific to a particular smoking status.For example,never-smokers displayed a greater proportion of hypomethylated differentially methylated regions(hypoDMRs)and a greater number of recurrently hypomethylated promoters,including those of ASPSCRI,TOP2A,DPP9,and USP39,all previously linked to cancer.Changes outside of promoters were also widespread and often recurrent,particularly methylation loss over repetitive elements,highly enriched for ERV1 subfamilies.Recurrent hypoDMRs were enriched for several transcription factor binding motifs,often for genes involved in signaling and cell proliferation.For example,71%of recurrent promoter hypoDMRs contained a motif for NKX2-1.Finally,the majority of DMRs were located within an active chromatin state in tissues profiled using the Roadmap Epigenomics data,suggesting that methylation changes may contribute to altered regulatory programs through the adaptation of cell type-specific expression programs.展开更多
The specificity of protein-DNA interactions is most commonly modeled using position weight matrices （PWMs）. First introduced in 1982, they have been adapted to many new types of data and many different approaches ha...The specificity of protein-DNA interactions is most commonly modeled using position weight matrices （PWMs）. First introduced in 1982, they have been adapted to many new types of data and many different approaches have been developed to determine the parameters of the PWM. New high-throughput technologies provide a large amount of data rapidly and offer an unprecedented opportunity to determine accurately the specificities of many transcription factors （TFs）. But taking full advantage of the new data requires advanced algorithms that take into account the biophysical processes involved in generating the data. The new large datasets can also aid in determining when the PWM model is inadequate and must be extended to provide accurate predictions of binding sites. This article provides a general mathematical description of a PWM and how it is used to score potential binding sites, a brief history of the approaches that have been developed and the types of data that are used with an emphasis on algorithms that we have developed for analyzing high-throughput datasets from several new technologies. It also describes extensions that can be added when the simple PWM model is inadequate and further enhancements that may be necessary, it briefly describes some applications of PWMs in the discovery and modeling of in vivo regulatory networks.展开更多
基金supported by funds from Fudan Universityfunds from Rijk Zwaan,the Netherlands,and the Biology Department and the Huck Institutes of the Life Sciences at the Pennsylvania State University in USA
文摘In eukaryotes, crossovers together with sister chromatid cohesion maintain physical association between homologous chromosomes, ensuring accurate chromosome segregation during meiosis I and resulting in exchange of genetic information between homologues. The Arabidopsis PTD （Parting Dancers） gene affects the level of meiotic crossover formation, but its functional relationships with other core meiotic genes, such as AtSP011-1, AtRAD51, and AtMSH4, are unclear; whether PTD has other functions in meiosis is also unknown. To further analyze PTD function and to test for epistatic relationships, we compared the meiotic chromosome behaviors ofAtspoll-1 ptd and Atrad51 ptd double mutants with the relevant single mutants. The results suggest that PTD functions downstream of AtSP011-1 and AtRAD51 in the meiotic recombination pathway. Furthermore, we found that meiotic defects in rck pM and Atmsh4 ptd double mutants showed similar meiotic phenotypes to those of the relevant single mutants, providing genetic evidences for roles of PTD and RCK in the type I crossovers pathway. Moreover, we employed a pollen tetrad-based fluorescence method and found that the meiotic crossover frequencies in two genetic intervals were significantly reduced from 6.63% and 22.26% in wild-type to 1.14% and 6.36%, respectively, in the ptd^2 mutant. These results revealed new aspects of PTD function in meiotic crossover formation.
基金Supported by The National R and D Program for Cancer ControlMinistry of Health and Welfare+1 种基金South KoreaNo.1120330
文摘AIM:To evaluate the association between the geneticpolymorphisms and haplotypes of the ITGA1 gene and the risk of gastric cancer.METHODS:The study subjects were 477 age-and sex-matched case-control pairs.Genotyping was performed for 15 single nucleotide polymorphisms(SNPs)in ITGA1.The associations between gastric cancer and these SNPs and haplotypes were analyzed with multivariate conditional logistic regression models.Multiple testing corrections were carried out following methodology for controlling the false discovery rate.Gene-based association tests were performed using the versatile gene-based association study(VEGAS)method.RESULTS:In the codominant model,the ORs for SNPs rs2432143(1.517;95%CI:1.144-2.011)and rs2447867(1.258;95%CI:1.051-1.505)were statistically significant.In the dominant model,polymorphisms of rs1862610 and rs2447867 were found to be significant risk factors,with ORs of 1.337(95%CI:1.029-1.737)and 1.412(95%CI:1.061-1.881),respectively.In the recessive model,only the rs2432143 polymorphism was significant(OR=1.559,95%CI:1.150-2.114).The C-C type of ITGA1 haplotype block 2 was a significant protective factor against gastric cancer in the both codominant model(OR=0.602,95%CI:0.212-0.709,P=0.021)and the dominant model(OR=0.653,95%CI:0.483-0.884).The ITGA1 gene showed a significant gene-based association with gastric cancer in the VEGAS test.In the dominant model,the A-T type of ITGA1 haplotype block 2 was a significant risk factor(OR=1.341,95%CI:1.034-1.741).SNP rs2447867 might be related to the severity of gastric epithelial injury due to inflammation and,thus,to the risk of developing gastric cancer.CONCLUSION:ITGA1 gene SNPs rs1862610,rs2432143,and rs2447867 and the ITGA1 haplotype block that includes SNPs rs1862610 and rs2432143 were significantly associated with gastric cancer.
基金Supported by A grant from the National R&D Program for Cancer Control,Ministry of Health and Welfare,South Korea,No.1120330
文摘AIM: To evaluate the association between genetic polymorphisms of the gene encoding AMP-activated protein kinase (PRKAA1) and the risk of gastric cancer.
基金supported in part by the Siteman Cancer Center Precision Medicine Pathway(Grant No.T32CA113275)Erica C.Pehrsson is supported by a Postdoctoral Fellowship from the American Cancer Society(Grant No.PF-17-201-01)+6 种基金Erica C.Pehrsson,Jennifer A.Karlow,and Ting Wang are supported by the National Institutes of Health[Grant Nos.R01HG007354(National Human Genome Research Institute)R01HG007175(National Human Genome Research Institute)R01ES024992(National Institute of Environmental Health Sciences)U01CA200060(National Cancer Institute)U24ES026699(National Institute of Environmental Health Sciences)U01HG009391(National Human Genome Research Institute)]the American Cancer Society(Grant No.RSG-14-049-01-DMC).
文摘Epigenetic alterations are widespread in cancer and can complement genetic alterations to influence cancer progression and treatment outcome.To determine the potential contribution of DNA methylation alterations to tumor phenotype in non-small cell lung cancer(NSCLC)in both smoker and never-smoker patients,we performed genome-wide profiling of DNA methylation in 17 primary NSCLC tumors and 10 matched normal lung samples using the complementary assays,methylated DNA immunoprecipitation sequencing(MeDIP-seq)and methylation sensitive restric-tion enzyme sequencing(MRE-seq).We reported recurrent methylation changes in the promoters of several genes,many previously implicated in cancer,including FAM83A and SEPT9(hy-pomethylation),as well as PCDH7,NKX2-1,and SOX17(hypermethylation).Although many methylation changes between tumors and their paired normal samples were shared across patients,several were specific to a particular smoking status.For example,never-smokers displayed a greater proportion of hypomethylated differentially methylated regions(hypoDMRs)and a greater number of recurrently hypomethylated promoters,including those of ASPSCRI,TOP2A,DPP9,and USP39,all previously linked to cancer.Changes outside of promoters were also widespread and often recurrent,particularly methylation loss over repetitive elements,highly enriched for ERV1 subfamilies.Recurrent hypoDMRs were enriched for several transcription factor binding motifs,often for genes involved in signaling and cell proliferation.For example,71%of recurrent promoter hypoDMRs contained a motif for NKX2-1.Finally,the majority of DMRs were located within an active chromatin state in tissues profiled using the Roadmap Epigenomics data,suggesting that methylation changes may contribute to altered regulatory programs through the adaptation of cell type-specific expression programs.
文摘The specificity of protein-DNA interactions is most commonly modeled using position weight matrices （PWMs）. First introduced in 1982, they have been adapted to many new types of data and many different approaches have been developed to determine the parameters of the PWM. New high-throughput technologies provide a large amount of data rapidly and offer an unprecedented opportunity to determine accurately the specificities of many transcription factors （TFs）. But taking full advantage of the new data requires advanced algorithms that take into account the biophysical processes involved in generating the data. The new large datasets can also aid in determining when the PWM model is inadequate and must be extended to provide accurate predictions of binding sites. This article provides a general mathematical description of a PWM and how it is used to score potential binding sites, a brief history of the approaches that have been developed and the types of data that are used with an emphasis on algorithms that we have developed for analyzing high-throughput datasets from several new technologies. It also describes extensions that can be added when the simple PWM model is inadequate and further enhancements that may be necessary, it briefly describes some applications of PWMs in the discovery and modeling of in vivo regulatory networks.
